def run(self):
"""
Run stuff and save stuff.
:return:
"""
out_dir = "out_directories"
name = "test1.avi"
out_path = os.path.abspath(os.path.join(out_dir, name))
self.make_temp_directory(out_dir)
self.ani.run(out_path, 10, 10)
self.estmd.open_movie(out_path)
self.estmd.run(by_frame=True)
self.estmd.run(by_frame=True)
frame_array = self.estmd.create_list_of_arrays()
n = len(frame_array)
for i in range(n):
frame_array[i] = {'frame': frame_array[i].flatten()}
self.cstmd.input = frame_array
self.cstmd.spike_trains = cstmd.run()[1]
# Adding simulation
sim = Simulation("Test", True)
sim.spike_trains = self.cstmd.spike_trains
sim.start_positions = 0
sim.pattern_duration = self.cstmd.duration
sim.num_afferents = self.cstmd.spike_trains.shape[0]
sim.duration = self.cstmd.spike_trains.shape[1]
sim.sampling_interval = math.ceil(self.cstmd.duration / 5)
n1 = sim.add_neuron(0.03125, .95, 300)
n2 = sim.add_neuron(0.03125, 0.91, 125)
n3 = sim.add_neuron(0.03125, 0.91, 125)
n1.connect(n2)
n1.connect(n3)
n2.connect(n3)
sim.run()
# Adding ActionSelection
pattern_input = []
for neuron in sim.neurons:
pattern_input.append(neuron.spike_times)
print "================================"
print pattern_input
print "================================"
a_s = ActionSelection(pattern_input=pattern_input,
pattern_duration=self.ani.total_frames,
animation=self.ani)
a_s.run(show_plots=False)
def test_general_test(self):
"""
One mothafacka test to rule them all.
:return:
"""
sim = Simulation()
sim.load_file("sample")
n1 = sim.add_neuron(0.03125, .95, 300)
sim.run()
sim.plot_weight_distributions()
sim.plot_membrane_potential()
def test_general_test(self):
"""
One mothafacka test to rule them all.
:return:
"""
sim = Simulation()
sim.load_file("sample")
n1 = sim.add_neuron(0.03125, .95, 300)
sim.run()
sim.plot_weight_distributions()
sim.plot_membrane_potential()
def run_simulation(self,
sample,
cursor,
num_neurons,
description,
a_plus,
a_ratio,
theta,
weights,
training,
return_object=False):
sim = Simulation(description, training)
sim.load_sample(sample, cursor)
# Add the post-synaptic neurons and connect them.
neurons = []
for i in range(num_neurons):
if weights is not None:
weight = weights[i]
else:
weight = None
n = sim.add_neuron(a_plus, a_ratio, theta, weight)
for neuron in neurons:
n.connect(neuron)
neurons.append(n)
sim.run()
# Allow for simulations run on generated samples.
if 'animation_id' not in sample:
sample['animation_id'] = None
if 'estmd_id' not in sample:
sample['estmd_id'] = None
_id = self.save(sim,
sample['animation_id'],
sample['estmd_id'],
sample['_id'])
return _id
def run_simulation(self,
sample,
cursor,
num_neurons,
description,
a_plus,
a_ratio,
theta,
weights,
training,
return_object=False):
sim = Simulation(description, training)
sim.load_sample(sample, cursor)
# Add the post-synaptic neurons and connect them.
neurons = []
for i in range(num_neurons):
if weights is not None:
weight = weights[i]
else:
weight = None
n = sim.add_neuron(a_plus, a_ratio, theta, weight)
for neuron in neurons:
n.connect(neuron)
neurons.append(n)
sim.run()
# Allow for simulations run on generated samples.
if 'animation_id' not in sample:
sample['animation_id'] = None
if 'estmd_id' not in sample:
sample['estmd_id'] = None
_id = self.save(sim, sample['animation_id'], sample['estmd_id'],
sample['_id'])
return _id
